「que」の検索結果

Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).

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Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

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Hunt HTTP request smuggling (CL.TE, TE.CL, H2.CL, H2.TE). Cause: front-end proxy and back-end server disagree on where one request ends and the next begins (Content-Length vs Transfer-Encoding header parsing inconsistency). CL.TE: front-end uses CL, back uses TE → smuggle by sending TE: chunked but with body that fits CL count. TE.CL: opposite. H2.CL: HTTP/2 downgrade, smuggle CL into HTTP/1.1 back-end. Detection tools: Burp HTTP Request Smuggler extension, smuggler.py, h2csmuggler. Confirm: time-delay technique (smuggled GET with 30s timeout) — if front-end returns slow on next victim request, smuggling works. Validate: cache poisoning chain (smuggle request that gets cached for victim), credential theft (smuggle X-Forwarded-For override that captures next user's cookies), bypass auth (smuggled internal-path request). Real paid examples from major CDN deployments. Use when hunting H1 paid programs running CDN+origin stacks, when targeting load balancer / WAF bypass.

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Deeply analyze any empirical economics PDF using the five-question framework (五问框架): research question, identification strategy, core estimand, robustness logic, and scholarly contribution. Converts the PDF to Markdown, dispatches 5 parallel sub-agents (one per question), then compiles a 2000–3000 Chinese-character report with a transferable methodology checklist. Optimized for empirical economics papers in top journals (AER, QJE, JPE, RFS, RoF, JFE, etc.). Use this skill whenever the user wants to: - systematically analyze an empirical economics paper - apply the 五问框架 (five-question framework) to a paper - extract transferable research design elements from a top-journal paper - understand a paper's identification strategy, estimand, or robustness logic - run /five-questions, /five-q, or /paper-five-q-analysis - "帮我做五问分析", "用五问框架分析这篇论文", "analyze this paper with the five-question framework"

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Azure Queue Storage library for Rust. Send, receive, and manage queue messages. Triggers: "queue storage rust", "QueueClient rust", "send message rust", "receive messages rust", "QueueServiceClient rust", "queue rust".

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Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".

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Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.

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Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).

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Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

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Recherche académique dans les bases de données de doctrine juridique française, européenne et internationale. Utiliser cette skill lorsque l'utilisateur demande de rechercher des articles de doctrine, des thèses, des ouvrages académiques, ou des travaux universitaires en droit — y compris en droit comparé. Sources principales : ISIDORE (agrégateur SHS du CNRS incluant Cairn.info, Persée, OpenEdition), HAL (archive ouverte française), OpenAlex (base ouverte mondiale 250M+ travaux), Semantic Scholar, CrossRef, Persée (OAI direct), et CORE. Couvre la doctrine juridique francophone ET internationale avec workflow bilingue intégré pour le droit comparé. Déclencher aussi pour : bibliographie commentée, état de l'art, revue de littérature juridique, recherche d'auteur, vérification de citation, analyse bibliométrique.

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Modélise le raisonnement juridique d'un magistrat français pour l'analyse de dossiers civils. Utiliser ce skill pour analyser un litige et identifier les questions juridiques, construire un raisonnement juridique structuré (syllogisme), rédiger une motivation de jugement civil, rechercher la jurisprudence et les textes applicables via Judilibre et Légifrance, qualifier juridiquement des faits et actes, distinguer prétentions, moyens et arguments. Basé sur les Fiches méthodologiques de rédaction du jugement civil (ENM/Cour de cassation, 2023).

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La compétence Normalisation juridique FR nettoie un document Word rédigé en français juridique. Elle distingue deux régimes : les corrections déterministes (apostrophes courbes, guillemets français, espaces insécables, insécables des références — art. 1240, n° 21-12.345, 50 % —, ordinaux 1ère → 1re, ligatures œ, la loi dispose et non stipule, anglicismes-calques), appliquées directement ; et les réécritures de jugement (virgule avant « et », ternaires creux, formules emphatiques, harmonisations), posées en révisions Word soumises à votre acceptation. Chaque changement est consigné et restitué en tableau récapitulatif : on revient en arrière mot à mot — « défais 3, 7-10 ». La normalisation des citations reste purement formelle. Fonctionne en Cowork ou sur fichier Word.

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Archéologie des sources juridiques anciennes via Gallica (BNF, API stables, sans clé) : débats parlementaires du JO depuis 1871 (genèse des lois de la IIIe République), notes d'arrêt et doctrine d'origine dans les recueils Sirey et Dalloz, Bulletins civil et criminel anciens, Recueil Lebon, JO Lois et décrets 1881-2015, thèses et traités du XIXe-début XXe, Gazette des tribunaux, Bulletin de l'Inspection du travail. Déclencher dès que l'utilisateur cherche l'origine historique d'un texte, d'un concept ou d'une jurisprudence : « débats de la loi de 1884 », « note de Josserand », « que disait la doctrine en 1900 », « première apparition de la notion de… », « généalogie / histoire de l'article… », « arrêt ancien », « thèse d'avant-guerre », « Gallica », « BNF », ou toute recherche juridique antérieure aux bases modernes (Légifrance ne remonte pas avant l'époque contemporaine, Judilibre non plus). Déclencher aussi en COMPLÉMENT de travaux-preparatoires quand la loi étudiée est antérieure à 1958.

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Assistant juridique expert en droit français. Recherches, consultations, rédaction d'actes, contre-argumentaires, analyses de contrats et de pièces, veille, vérification et harmonisation de références. Produit des documents Word (computer use) ou des réponses conversationnelles structurées. MCP : OpenLegi, Themia, LegalDataHunter. Déclencher pour toute question juridique, tout concept de droit français (contrat, responsabilité, jurisprudence, code civil, tribunal, indemnisation), toute question sur le droit de l'UE, la CJUE ou la CEDH, et toute demande de vérification ou harmonisation de références bibliographiques juridiques.

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Expert blueprint for quest tracking systems (objectives, progress, rewards, branching chains) using Resource-based quests, signal-driven updates, and AutoLoad managers. Use when implementing RPG quests or mission systems. Keywords quest, objectives, Quest Resource, QuestObjective, signal-driven, branching, rewards, AutoLoad.

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Find which ChatGPT search queries mention a given brand. Tests long-tail queries against ChatGPT's web-search-enabled model and reports which ones surface the brand. Use when the user asks to "find queries for [brand]", "check GEO visibility", "which queries mention [brand]", "geo query finder", "find AI mentions", or "test ChatGPT queries for [brand]".

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Parquet格式化器。用于加载和格式化parquet类型的文件。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到Parquet格式化、Parquet数据加载、读取Parquet文件、Parquet转JSONL等需求时使用此skill。

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Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

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Optimize slow database queries — analyze execution plans, add indexes, rewrite queries. Use when asked about "slow query", "optimize SQL", "query performance", or "explain this query".

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Track Claude's best and worst questions in DuckDB. Use when Claude asks a notably good or bad question, or to review question quality. Triggers: leaderboard, bad question, good question, question quality.

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Run SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Auto-activate for litestar_queues, QueuePlugin, QueueConfig, WorkerConfig, QueueService, @task, QueuedBackgroundTask, QueueEventsConfig, or SQLSpecBackendConfig. Not for litestar-saq, Celery, or Dramatiq.

日本語の概要は準備中です。原文の説明を表示しています。